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<header><h1><a href="/">ccv</a></h1>
<p>A Modern Computer Vision Library</p>
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<section><h1>closing the gap between open source and proprietary</h1>
<p>April 25th, 2014</p>
<p>In 0.6 release, ccv’s deep learning based image classifier achieved 16.26% top-5 missing rate on imageNet 2010. However, the state of the art uses imageNet 2012 data set as the standard, and it is hard to do apple to orange comparison.</p>

<p>For the past 3 weeks, I was able to obtain the imageNet 2012 dataset, therefore, do the apple to apple comparison with the state of the art.</p>

<p>The newly trained data model on imageNet 2012 was able to obtain 16.22% top-5 missing rate on imageNet 2012 dataset, which is about 3% better than <a href="http://caffe.berkeleyvision.org/">Caffe</a>’s implementation, and about 0.55% shying away from 1-convnet implementation from <a href="http://cilvr.nyu.edu/doku.php?id=software:overfeat:start">OverFeat</a>. This implementation is still 5% behind the state of the art <a href="http://www.clarifai.com">Clarifai</a> though.</p>

<p>This is a good step towards closing the gap between open source implementation and proprietary implementation.</p>

<p><img src="/photo/2014-04-25-dex.png" alt="dont-be-too-cute-dex" /></p>

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